Message output apparatus, learning apparatus, message output method, learning method, and program

ABSTRACT

Provided are a message output apparatus, a learning apparatus, a message output method, a learning method, and a program that allow a user to feel as if communication is being performed. An input section inputs, to a learned machine learning model that has been learned using learning data that includes learning input data including a plurality of consecutive frames of learning images and teaching data including a message associated with the learning input data, target input data including at least a plurality of consecutive frames of target images. A message identification section identifies a message according to output produced when the target input data is input to the machine learning model. A message output section outputs the identified message.

TECHNICAL FIELD

The present invention relates to a message output apparatus, a learningapparatus, a message output method, a learning method, and a program.

BACKGROUND ART

There have been known techniques to promote communication by allowingsharing of character strings of messages posted by users such as playersand audiences and audio of messages produced thereby, for example, inlive broadcasting of games, sports spectating, and the like.

SUMMARY TECHNICAL PROBLEM

Here, if a user such as player or audience can feel as if the abovecommunication is being performed even in a situation where he or she isalone, there is a prospect that the user in question will enjoy livebroadcasting of games, sports spectating, and the like more.

The present invention has been devised in light of the foregoingproblem, and it is an object of the present invention to provide amessage output apparatus, a learning apparatus, a message output method,a learning method, and a program that allow a user to feel as ifcommunication is being performed.

SOLUTION TO PROBLEM

In order to solve the above problem, a message output apparatusaccording to the present invention includes a learned machine learningmodel, an input section, a message identification section, and a messageoutput section. The learned machine learning model has been learnedusing learning data that includes learning input data including aplurality of consecutive frames of learning images and teaching dataincluding a message associated with the learning input data. The inputsection inputs, to the learned machine learning model, target input dataincluding at least a plurality of consecutive frames of target images.The message identification section identifies a message according tooutput produced when the target input data is input to the machinelearning model. The message output section outputs the identifiedmessage.

In a mode of the present invention, the input section inputs, to themachine learning model, the target input data generated while a game isplayed and including at least the plurality of consecutive frames oftarget images representing a playing status of the game in question, andthe message output section outputs the identified message while the gameis played.

In this mode, the learning input data may further include informationregarding a player associated with the learning image, and the targetinput data may further include information regarding the player who isplaying the game.

Here, the player information may include information regardingcontroller inputs made by the player.

In this case, the player information may include a value representing afrequency of controller inputs.

Also, in this mode, the player information may include a captured faceimage of the player.

Also, in a mode of the present invention, the learning input dataincludes a message different from the message represented by theteaching data, and the target input data further includes a messagealready output from the message output section.

Also, a learning apparatus according to the present invention includes alearning data acquisition section and a learning section. The learningdata acquisition section acquires learning data that includes learninginput data including a plurality of consecutive frames of learningimages and teaching data including a message associated with thelearning input data. The learning section learns a machine learningmodel using the learning data.

A mode of the present invention further includes a learning datageneration section that generates the learning data on the basis ofdelivery data representing a delivery status of a game that is beingdelivered or was delivered.

Also, a message output method according to the present inventionincludes a step of inputting, to a learned machine learning model thathas been learned using learning data including learning input dataincluding a plurality of consecutive frames of learning images andteaching data including a message associated with the learning inputdata, target input data including at least a plurality of consecutiveframes of target images, a step of identifying a message according tooutput produced when the target input data is input to the machinelearning model, and a step of outputting the identified message.

Also, a learning method according to the present invention includes astep of acquiring learning data including learning input data includinga plurality of consecutive frames of learning images and teaching dataincluding a message associated with the learning input data and a stepof learning a machine learning model using the learning data.

Also, a program according to the present invention causes a computer toperform a procedure of inputting, to a learned machine learning modelthat has been learned using learning data including learning input dataincluding a plurality of consecutive frames of learning images andteaching data including a message associated with the learning inputdata, target input data including at least a plurality of consecutiveframes of target images, a procedure of identifying a message accordingto output produced when the target input data is input to the machinelearning model, and a procedure of outputting the identified message.

Also, another program according to the present invention causes acomputer to perform a procedure of acquiring learning data includinglearning input data including a plurality of consecutive frames oflearning images and teaching data including a message associated withthe learning input data and a procedure of learning a machine learningmodel using the learning data.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram illustrating an example of an overall configurationof an entertainment system according to an embodiment of the presentinvention.

FIG. 2 is a diagram illustrating a configuration example of theentertainment apparatus according to the embodiment of the presentinvention.

FIG. 3 is a diagram illustrating an example of a game screen.

FIG. 4 is a functional block diagram illustrating examples of functionsimplemented in the entertainment apparatus according to the embodimentof the present invention.

FIG. 5 is a diagram schematically illustrating an example of learningdata.

FIG. 6 is a flowchart illustrating an example of a flow of a learningprocess handled by the entertainment apparatus according to theembodiment of the present invention.

FIG. 7 is a flowchart illustrating an example of a flow of a messageoutput process handled by the entertainment apparatus according to theembodiment of the present invention.

DESCRIPTION OF EMBODIMENT

A detailed description will be given below of an embodiment of thepresent invention on the basis of drawings.

FIG. 1 is a diagram illustrating an example of an overall configurationof an entertainment system 10 according to the embodiment of the presentinvention. The entertainment system 10 according to the presentembodiment includes an entertainment apparatus 12, a display 14, cameras16, microphones 18, a controller 20, and the like.

The entertainment apparatus 12 according to the present embodiment is,for example, a computer such as a game console, a DVD (Digital VersatileDisc) player, or a Blu-ray (registered trademark) player. Theentertainment apparatus 12 according to the present embodiment generatesvideo and audio by executing a stored game program or a game programrecorded on an optical disc, reproducing content, or by other means.Then, the entertainment apparatus 12 according to the present embodimentoutputs, to the display 14, a video signal representing generated videoand an audio signal representing generated audio.

The entertainment apparatus 12 according to the present embodimentincludes, for example, a processor 30, a storage section 32, acommunication section 34, and an input/output section 36 as illustratedin FIG. 2.

The processor 30 is a program-controlled device such as a CPU (CentralProcessing Unit) that operates in accordance with a program installed inthe entertainment apparatus 12. The processor 30 according to thepresent embodiment also includes a GPU (Graphics Processing Unit) thatdraws an image to a frame buffer on the basis of a graphics command anddata supplied from the CPU.

The storage section 32 is, for example, a storage element such as a ROM(Read-Only Memory) or a RAM (Random Access Memory), a hard disk drive,or the like. The storage section 32 stores programs executed by theprocessor 30 or the like. Also, the storage section 32 according to thepresent embodiment has a frame buffer area where the GPU draws an image.

The communication section 34 is, for example, a communication interfacesuch as a wireless LAN (Local Area Network) module.

The input/output section 36 is an input/output port such as an HDMI(registered trademark) (High-Definition Multimedia Interface) port or aUSB (Universal Serial Bus) port.

The display 14 is, for example, a liquid crystal display and displaysvideo represented by a video signal supplied from the entertainmentapparatus 12. Also, the display 14 outputs audio represented by an audiosignal output from the entertainment apparatus 12.

Each of the cameras 16 is, for example, a device such as a digitalcamera that outputs, to the entertainment apparatus 12, datarepresenting how it looks around the camera 16 such as captured image ofa subject.

Each of the microphones 18 is a device that acquires surrounding audioand outputs audio data representing the audio to the entertainmentapparatus 12.

The entertainment apparatus 12 and the display 14 are connected, forexample, via an HDMI cable. The entertainment apparatus 12, the cameras16, and the microphones 18 are connected, for example, via AUX(Auxiliary) cables.

The controller 20 is an operation input apparatus for making operationinputs to the entertainment apparatus 12. The user can make a variety ofoperation inputs with the controller 20 by pressing directional keys andbuttons and tilting operation sticks of the controller 20. Then, in thepresent embodiment, the controller 20 outputs input data associated withoperation inputs to the entertainment apparatus 12. Also, the controller20 according to the present embodiment includes a USB port. Then, thecontroller 20 can output input data to the entertainment apparatus 12 ina wired manner by connecting to the entertainment apparatus 12 with aUSB cable. Also, the controller 20 according to the present embodimentincludes a wireless communication module or the like, thus allowing tooutput input data to the entertainment apparatus 12 in a wirelessmanner.

Also, the controller 20 may include an acceleration sensor, apressure-sensitive sensor, a touch pad, or the like. Then, thecontroller 20 may send, to the entertainment apparatus 12, sensing datarepresenting a measured value of a sensor included in the controller 20.

In the present embodiment, for example, as the entertainment apparatus12 executes a game program, video and audio representing the gameplaying status are generated. Then, the video in question appears on thedisplay 14 watched by the player of the game in question whereas theaudio is output from the display 14.

FIG. 3 is a diagram illustrating an example of a game screen 40displayed on the display 14 in the present embodiment. In the exampleillustrated in FIG. 3, a playing status image 42 that is a frame imagerepresenting the game playing status is disposed on the left in the gamescreen 40. Also, for example, an image of a character 44 that broadcastslive the game playing status is disposed at the upper right corner ofthe game screen 40. Then, a character string representing a messageaccording to the game playing status is displayed in a lower-rightmessage area 46 as a line spoken by the character 44 in question, andaudio representing the message in question is output from the display14.

Then, in the present embodiment, for example, a message is output thatis identified by using a learned machine learning model such as neuralnetwork or support vector machine. A description will be given below offunctions of and processes performed by the entertainment apparatus 12according to the present embodiment with emphasis on learning of themachine learning model in question and output of messages using thelearned machine learning model.

FIG. 4 is a functional block diagram illustrating examples of functionsincorporated in the entertainment apparatus 12 according to the presentembodiment. It should be noted that there is no need to implement allthe functions illustrated in FIG. 4 in the entertainment apparatus 12according to the present embodiment and that functions other than thoseillustrated in FIG. 4 may be implemented.

As illustrated in FIG. 4, the entertainment apparatus 12 functionallyincludes, for example, a machine learning model 50, a delivery dataacquisition section 52, a learning data generation section 54, alearning data storage section 56, a learning data acquisition section58, a learning section 60, a target input data generation section 62, atarget input data acquisition section 64, an input section 66, a messageidentification section 68, and a message output section 70.

The machine learning model 50 is mainly implemented by the processor 30and the storage section 32. The delivery data acquisition section 52 ismainly implemented by the processor 30 and the communication section 34.The learning data generation section 54, the learning data acquisitionsection 58, the learning section 60, the target input data generationsection 62, the target input data acquisition section 64, the inputsection 66, and the message identification section 68 are mainlyimplemented by the processor 30. The learning data storage section 56 ismainly implemented by the storage section 32. The message output section70 is mainly implemented by the processor 30 and the input/outputsection 36.

The functions of the machine learning model 50, the delivery dataacquisition section 52, the learning data generation section 54, thelearning data storage section 56, the learning data acquisition section58, and the learning section 60 are equivalent to those of a learningapparatus that learns the machine learning model 50.

The functions of the machine learning model 50, the target input datageneration section 62, the target input data acquisition section 64, theinput section 66, the message identification section 68, and the messageoutput section 70 are equivalent to those of a message output apparatusthat outputs a message using the learned machine learning model 50.

The above functions may be implemented by executing, with the processor30, a program that has been installed in the entertainment apparatus 12,a computer, and includes instructions corresponding to the abovefunctions. This program may be supplied via a computer-readableinformation storage medium such as an optical disc, a magnetic disk, amagnetic tape, a magneto-optical disk, or a flash memory, or via theInternet or other means.

The machine learning model 50 is, for example, a machine learning modelsuch as a neural network or a support vector machine in the presentembodiment.

The delivery data acquisition section 52 acquires, for example, deliverydata representing a delivery status of a game that is being delivered orwas delivered from a game live video delivery site or other site in thepresent embodiment.

The learning data generation section 54 generates, for example, learningdata 80 schematically illustrated in FIG. 5 in the present embodiment.Here, for example, the learning data 80 may be generated on the basis ofdelivery data acquired by the delivery data acquisition section 52.

The learning data generation section 54 extracts, from delivery data,messages that occur including character strings of messages posted byusers such as players and audiences and displayed on the screen, audiomessages produced by users such as players and audiences, and the like.The messages in question will be hereinafter referred to as learningmessages 82. Here, for example, a message that occurs a given amount oftime or more from when an immediately previous message occurred may beextracted as the learning message 82. Also, for example, a given numberof messages that occur consecutively a given amount of time or more fromwhen an immediately previous message occurred may be extracted as thelearning messages 82. Alternatively, for example, a series of messagesthat occur within a given amount of time may be extracted as thelearning messages 82.

Then, the learning data generation section 54 identifies, for example, aframe equivalent to a time when an extracted message occurred. Here, inthe case where a plurality of messages are extracted, for example, theframe equivalent to the time when the first message occurred may beidentified. We assume here that an identified frame number is “n.” Then,the learning data generation section 54 extracts, from the deliverydata, (a+b) frame images whose frame numbers are equal to or larger than(n−a+1) and equal to or smaller than (n+b). Frame images extracted inthis manner will be hereinafter referred to as learning images 84.

Then, the learning data generation section 54 generates, for example,the learning data 80 that includes the plurality of consecutive framesof learning images 84 as learning input data and the extracted learningmessage 82 as teaching data. Here, the learning data generation section54 may generate the learning data 80 that includes the plurality ofconsecutive frames of learning image 84 representing a game playingstatus as learning input data and the extracted learning message 82 asteaching data. Here, for example, the learning data 80 that includes the(a+b) learning images 84 as learning input data is generated. In thisexample, the extracted learning message 82, teaching data included inthe learning data 80, is a message included in the learning data 80 andissued while the (a+b) consecutive frames of learning image 84 aredisplayed. Then, the learning data generation section 54 stores thegenerated learning data 80 in the learning data storage section 56.

It should be noted that, as will be described later, teaching dataincluded in the learning data 80 may include a label representingemotion when the message in question is issued.

Also, teaching data included in the learning data 80 may include a labelrepresenting whether or not a message has been issued.

In this case, the learning data generation section 54 may generate, forexample, the learning data 80 that includes the plurality of consecutiveframes of learning images 84 as learning input data and the extractedlearning message 82 as teaching data and a label representing issuanceof a message as teaching data.

Also, the learning data generation section 54 may extract, from deliverydata, a plurality of frame images (e.g., (a+b) frame images) while nomessage is issued as the learning image 84. Then, the learning datageneration section 54 may generate the learning data 80 that includesthe extracted learning image 84 as learning input data and a labelrepresenting that no message has been issued as teaching data.

The learning data storage section 56 stores, for example, the learningdata 80 that includes learning input data and teaching data in thepresent embodiment. Here, learning input data may include the pluralityof consecutive frames of learning images 84. Also, teaching data mayinclude the learning message 82 associated with the learning input datain question (for example, the learning message 82 issued while thelearning image 84 in question is displayed).

The learning data acquisition section 58 acquires, for example, thelearning data 80 stored in the learning data storage section 56 in thepresent embodiment.

The learning section 60 learns the machine learning model 50 using thelearning data 80 acquired by the learning data acquisition section 58.Here, for example, supervised learning that updates a parameter valueset in the machine learning model 50 may be carried out on the basis ofa difference between output produced when learning input data includedin the learning data 80 is input to the machine learning model 50 andteaching data included in the learning data 80.

The target input data generation section 62 generates, for example,target input data including at least a plurality of consecutive framesof target images in the present embodiment. Here, the target input datageneration section 62 may generate, while a game is played, target inputdata including at least a plurality of consecutive frames of targetimages representing the playing status of the game. Here, we assume, forexample, that the frame number of the playing status image 42 beingdisplayed is “m.” In this case, most recently displayed (a+b) frames ofthe playing status image 42 are acquired whose frame numbers are equalto or larger than (m−a−b+1) and equal to or smaller than m. Then, targetinput data that includes (a+b) frames of the playing status image 42 inquestion as a target image is generated.

The target input data acquisition section 64 acquires, for example,target input data generated by the target input data generation section62 in the present embodiment.

The input section 66 inputs, for example, the target input data acquiredby the target input data acquisition section 64 to the learned machinelearning model 50 in the present embodiment.

The message identification section 68 identifies, for example, a messageaccording to output produced when the target input data acquired by thetarget input data acquisition section 64 is input to the learned machinelearning model 50 in the present embodiment.

The message output section 70 outputs, for example, the messageidentified by the message identification section 68 in the presentembodiment. Here, the message output section 70 may output theidentified message while a game is played. The message output section 70may display, for example, a character string representing the messageidentified by the message identification section 68 in the message area46. Also, the message output section 70 may output generated audio byusing an audio synthesis technique on the basis of the messageidentified by the message identification section 68.

In the present embodiment, a message according to the game playingstatus is output using the learned machine learning model 50 that haslearned character strings of messages posted by users such as playersand audiences and audio of messages produced thereby. Thus, according tothe present embodiment, even in a situation where a player is playingalone, he or she can feel as if communication is being performed whilethe game is played.

It should be noted that the present invention is not limited inapplication to the output of a message to a player. For example, thepresent embodiment may be applied to output a message according to aplaying status to an audience viewing game playing video or a messageaccording to a match status to an audience watching sports match video.In these cases, users such as audiences and viewers can also feel as ifcommunication is being performed.

It should be noted that in the case where the number of images that canbe input to the machine learning model 50 is fixed, the number oflearning images 84 included in learning input data and the number oftarget images included in target input data are required to be the samegiven number. Here, in the case where the machine learning model 50 is aneural network, the number of image frames that can be input may bevariable depending on the type of the neural network. In such a case,the number of learning images 84 included in each of the plurality ofpieces of learning data 80 and the number of target images included intarget input data need not be the same.

Here, the above delivery data may include, for example, informationregarding a player playing a game. The player information in questionmay be collected, for example, from the entertainment system 10connected to a game live video delivery site. Then, for example, thelearning data generation section 54 may generate the learning data 80that further includes player information associated with the learningimage 84 in learning input data. Then, in this case, the target inputdata generation section 62 may generate target input data that furtherincludes information regarding the player playing the game.

Player information included in learning input data may include, forexample, information regarding controller inputs made by the player suchas key log data. For example, the learning data generation section 54may identify information regarding controller inputs made by the playerwhile the plurality of frames of learning images 84 included in thelearning data 80 are displayed. Then, the learning data generationsection 54 may generate the learning data 80 that further includesinformation regarding the identified controller inputs in learning inputdata.

In this case, the target input data generation section 62 may generatetarget input data that further includes information regarding the playerplaying the game while the plurality of most recent frames ((a+b) framesin the above example) of playing status images 42 are displayed. Forexample, target input data that further includes information regardingcontroller inputs made by the player playing the game during the periodof time in question may be generated.

Also, for example, the learning data generation section 54 may identifythe frequency of inputs made to the controller 20 while the plurality offrames of learning images 84 included in the learning data 80 aredisplayed on the basis of player control input information during theperiod of time in question. Here, for example, the number of inputs madeto the controller 20 per unit time during the period of time in questionmay be identified as an input frequency. Then, the learning datageneration section 54 may generate the learning data 80 that furtherincludes a value representing the input frequency in learning inputdata.

In this case, the target input data generation section 62 may identifythe frequency of inputs made to the controller 20 while the plurality ofmost recent frames of playing status images 42 are displayed on thebasis of player control input information during the period of time inquestion. Then, the learning data generation section 54 may generatetarget input data that further includes a value representing the inputfrequency in question.

Further, player's face images that are face images of the playercaptured by the cameras 16, for example, may be included in playerinformation that is included in learning input data. For example, thelearning data generation section 54 may generate the learning data 80that further includes, in learning input data, player's face imagescaptured while the plurality of frames of learning images 84 included inthe learning data 80 are displayed.

In this case, the target input data generation section 62 may generatetarget input data that further includes player's face images captured bythe camera 16 while the plurality of most recent frames of playingstatus images 42 are displayed.

Also, player information included in learning input data may include,for example, sensing data representing a measured value of the sensorincluded in the controller 20 while the plurality of frames of learningimages 84 included in the learning data 80 are displayed. Here, asdescribed above, the sensor in question may be an acceleration sensor, apressure-sensitive sensor, a touch pad, or the like. Also, as describedabove, the sensing data in question may be sent from the controller 20to the entertainment apparatus 12. In this case, the target input datageneration section 62 may generate target input data that furtherincludes sensing data representing a measured value of the sensorincluded in the controller 20 while the plurality of most recent framesof playing status images 42 are displayed.

Also, for example, the player information in question in a game playedwith a head-mounted display (HMD) worn may include sensing data that canbe acquired from the HMD and that represents a measured value of thesensor such as line-of-sight sensor or acceleration sensor included inthe HMD. For example, the learning data 80 that further includes, inlearning input data, sensing data acquired from the HMD while theplurality of frames of learning images 84 included in the learning data80 are displayed may be generated. In this case, the target input datageneration section 62 may generate target input data that furtherincludes sensing data acquired from the HMD while the plurality of mostrecent frames of playing status images 42 are displayed.

For example, player information acquired as described above ispresumably different between the case where the game status is laid-backand the case where the game status is urgent. Specifically, for example,the frequency of inputs to the controller 20 is presumably higher in thecase where the game status is urgent than in the case where the gamestatus is laid-back. Also, for example, the controller 20, the line ofsight, and the HMD presumably move more intensely in the case where thegame status is urgent than in the case where the game status islaid-back. Also, for example, the player's facial expression isdifferent between the case where the game status is laid-back and thecase where the game status is urgent. Thus, player information is highlylikely to correspond to the message to be output. For this reason, forexample, there is a prospect that a more accurate message will be outputfrom the machine learning model 50 by using the above player informationto learn the machine learning model 50.

Also, learning input data may include a message different from thelearning message 82 included in the learning data 80 as teaching data.Here, for example, the learning data generation section 54 may identifymessages that occur from a given period of time preceding the time whenthe learning message 82 included in the learning data 80 as teachingdata occurs to the time when the learning message 82 in question occurs.Then, the learning data generation section 54 may generate the learningdata 80 that further includes the identified messages in learning inputdata. In this case, the target input data generation section 62 maygenerate target input data that further includes messages output fromthe message output section 70 from a given period of time earlier up tothe present time.

A message is often issued in response to a message issued earlier thanthe message in question. Therefore, there is a prospect that a moreaccurate message will be output from the machine learning model 50, forexample, by using a message different from the message in question suchas a message that occurred earlier than the message in question to learnthe machine learning model 50.

Also, learning input data may include data representing a title or typeof the game being played. In this case, the target input data generationsection 62 may generate target input data that includes datarepresenting the title or type of the game being played.

Also, as described above, in the case where the teaching data includedin the learning data 80 includes a label representing an emotion, themessage identification section 68 identifies the emotion on the basis ofthe output from the learned machine learning model 50. Then, the messageoutput section 70 may output a message according to the identifiedemotion. For example, audio representing the identified emotion may beoutput. Also, for example, the character 44 that moves in a manneraccording to the identified emotion may be displayed.

Here, for example, the learning data generation section 54 may estimatethe emotion on the basis of the above player information. Then, thelearning data generation section 54 may generate the learning data 80that further includes a label representing the estimated emotion inteaching data.

A description will be given here of an example of a flow of a learningprocess of learning the machine learning model 50 handled by theentertainment apparatus 12 according to the present embodiment withreference to the flowchart illustrated in FIG. 6. We assume here thatthe plurality of pieces of learning data 80 are stored in the learningdata storage section 56.

First, the learning data acquisition section 58 acquires a piece ofdata, not used to learn the machine learning model 50, from the learningdata 80 stored in the learning data storage section 56 (S101).

Then, the learning section 60 learns the machine learning model 50 usingthe learning data 80 acquired in the process in S101 (S102).

Then, the learning section 60 confirms whether or not all the learningdata 80 stored in the learning data storage section 56 has beensubjected to the process in S102 (S103).

Here, in the case where it is confirmed that all the learning data 80stored in the learning data storage section 56 has yet to be subjectedto the process in S102 (N in S103), the process returns to the processin step S101.

In the case where it is confirmed that all the learning data 80 storedin the learning data storage section 56 has been subjected to theprocess in S102 (Y in S103), the process illustrated in the presentprocessing example is terminated.

A description will be given next of an example of a message outputprocess using the learned machine learning model 50 handled by theentertainment apparatus 12 according to the present embodiment withreference to the flowchart illustrated in FIG. 7. In the presentprocessing example, we assume that the processes in S201 to S206 arerepeated at a frame rate at which the playing status image 42 isdisplayed.

First, the target input data generation section 62 generates targetinput data in the frame in question (S201).

Then, the target input data acquisition section 64 acquires the targetinput data generated in the process in S201 (S202).

Then, the input section 66 inputs the target input data, acquired in theprocess in S202, to the learned machine learning model 50 (S203).

Then, the message identification section 68 identifies a message to beoutput on the basis of the output of the machine learning model 50according to the input in the process in S203 (S204). A determinationresult may be made here as to whether or not an emotion or message hasoccurred as described above.

Then, the message identification section 68 confirms whether or not themessage has been identified in the process in S204 (S205). In the casewhere it is confirmed that the message has not been identified (N inS205), the process returns to the process in step S201. In the casewhere it is confirmed that the message has been identified (Y in S205),the message output section 70 outputs the message identified in theprocess in S204 (S206), and the process returns to the process in stepS201. Here, an output according to the identified emotion may be outputas described above. Also, a determination result may be made as towhether or not a message has occurred.

In the above processing examples, for example, in the case where thefirst frame number of the playing status image 42 is “0,” while theplaying status image 42 whose frame number is equal to or larger than 0and equal to or smaller than (a+b-2) is displayed, the above processesmay not be performed.

Also, there is no need to repeat the processes in S201 to S206 everyframe as in the above processing example. For example, the processes inS201 to S206 may be performed randomly or at given time intervals.

It should be noted that the present invention is not limited to theabove embodiment.

Also, the above specific character strings and numbers and the abovespecific character strings and numbers in the figures are illustrative,and the present invention is not limited thereto.

1. A message output apparatus comprising: a learned machine learningmodel that has been learned using learning data that includes learninginput data including a plurality of consecutive frames of learningimages and teaching data including a message associated with thelearning input data; an input section adapted to input, to the learnedmachine learning model, target input data including at least a pluralityof consecutive frames of target images; a message identification sectionadapted to identify a message according to output produced when thetarget input data is input to the machine learning model; and a messageoutput section adapted to output the identified message.
 2. The messageoutput apparatus according to claim 1, wherein the input section inputs,to the machine learning model, the target input data generated while agame is played and including at least the plurality of consecutiveframes of target images representing a playing status of the game inquestion, and the message output section outputs the identified messagewhile the game is played.
 3. The message output apparatus according toclaim 2, wherein the learning input data further includes informationregarding a player associated with the learning image, and the targetinput data further includes information regarding the player who isplaying the game.
 4. The message output apparatus according to claim 3,wherein the player information includes information regarding controllerinputs made by the player.
 5. The message output apparatus according toclaim 4, wherein the player information includes a value representing afrequency of controller inputs.
 6. The message output apparatusaccording to claim 3, wherein the player information includes a capturedface image of the player.
 7. The message output apparatus according toclaim 1, wherein the learning input data further includes a messagedifferent from the message represented by the teaching data, and thetarget input data further includes a message already output from themessage output section.
 8. A learning apparatus comprising: a learningdata acquisition section adapted to acquire learning data that includeslearning input data including a plurality of consecutive frames oflearning images and teaching data including a message associated withthe learning input data; and a learning section adapted to learn amachine learning model using the learning data.
 9. The learningapparatus according to claim 8, further comprising: a learning datageneration section adapted to generate the learning data on a basis ofdelivery data representing a delivery status of a game that is beingdelivered or was delivered.
 10. A message output method comprising:inputting, to a learned machine learning model that has been learnedusing learning data including learning input data including a pluralityof consecutive frames of learning images and teaching data including amessage associated with the learning input data, target input dataincluding at least a plurality of consecutive frames of target images;identifying a message according to output produced when the target inputdata is input to the machine learning model; and outputting theidentified message.
 11. A learning method comprising: acquiring learningdata including learning input data including a plurality of consecutiveframes of learning images and teaching data including a messageassociated with the learning input data; and learning a machine learningmodel using the learning data.
 12. A non-transitory, computer readablestorage medium containing a program, which when executed by a computer,causes the computer to perform a message output method by carrying outactions, comprising: inputting, to a learned machine learning model thathas been learned using learning data including learning input dataincluding a plurality of consecutive frames of learning images andteaching data including a message associated with the learning inputdata, target input data including at least a plurality of consecutiveframes of target images; identifying a message according to outputproduced when the target input data is input to the machine learningmodel; and outputting the identified message.
 13. A non-transitory,computer readable storage medium containing a program, which whenexecuted by a computer, causes the computer to perform a learning methodby carrying out actions, comprising: acquiring learning data includinglearning input data including a plurality of consecutive frames oflearning images and teaching data including a message associated withthe learning input data; and learning a machine learning model using thelearning data.